{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Abstract\n",
    "- Research summary, findings, and next steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Overview\n",
    "- Problem statement, relevant literature, proposed methodology."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Processing\n",
    "- Pipeline details, data issues, assumptions/adjustments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data Analysis\n",
    " - Summary statistics, visualization, feature extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Model Training\n",
    "- Feature engineering, evaluation metrics, model selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Model Validation\n",
    " - Testing results, performance criteria, biases/risks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Conclusion\n",
    " - Positive/Negative results, recommendations, caveats/cautions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Data Sources\n",
    " - Links, downloads, access information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Source Code\n",
    " - Listings, documentation, dependencies (open-source)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: graphviz in /Users/eric/miniconda3/lib/python3.11/site-packages (0.20.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "!conda install -n py311 python==3.11\n",
    "!conda activate py311\n",
    "!pip install scikit-learn==1.5\n",
    "!pip install onnx==1.16\n",
    "!pip install scipy\n",
    "!pip install skl2onnx\n",
    "!pip install onnxruntime\n",
    "!pip install sklearn2pmml\n",
    "!pip install sklearn2pmml\n",
    "!pip install graphviz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Bibliography\n",
    "\n",
    "Example\n",
    "\n",
    "- 书籍：\n",
    "Goodfellow, I., Bengio, Y., and Courville, A. 2016. Deep Learning. MIT Press.\n",
    "- 期刊文章：\n",
    "LeCun, Y., Bengio, Y., and Hinton, G. 2015. Deep learning. Nature 521, 7553, 436-444.\n",
    "- 会议论文：\n",
    "Krizhevsky, A., Sutskever, I., and Hinton, G. E. 2012. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25, 1097-1105.\n",
    "- 在线资源：\n",
    "Chollet, F. 2017. Deep Learning with Python. Manning Publications. https://www.manning.com/books/deep-learning-with-python. Accessed July 1, 2024.\n",
    "- 技术报告：\n",
    "Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., et al. 2016. TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.\n",
    "- 博士论文：\n",
    "Silver, D. 2009. Reinforcement Learning and Simulation-Based Search in Computer Go. Ph.D. Dissertation. University of Alberta, Edmonton, AB, Canada.\n",
    "- 标准文档：\n",
    "ISO/IEC. 2018. ISO/IEC 30107-3:2017 Information technology — Biometric presentation attack detection — Part 3: Testing and reporting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
